Large Language Models (LLMs) and Generative AI are all the rage right now but will only work for organizations that have a solid grasp on the quality of their data and the series of operations acting upon that data to augment the base LLM.
A list o the best Data (and Analytic) Observability & Data Journey – Ideas and Background Links
In data and analytics, one skill stands timeless and universal: the art of blaming someone else when things go sideways. In this humorous blog, learn from the best!
Do you have data quality issues, a complex technical environment, and a lack of visibility into production systems?
These challenges lead to poor quality analytics and frustrated end users. Getting your data reliable is a start, but many other problems arise even if your data could be better. And your customers don’t care where the problem is in your toolchain. They want to know when to get their trusted dashboard refreshed (for example).
The uncertainty of not knowing where data issues will crop up next and the tiresome game of ‘who’s to blame’ when pinpointing the failure. It’s more than just a ‘last mile’ problem in data observability. It’s about personalization for your customers. Demanding Data Consumers require a personalized level of Observability.
Data teams have out-of-control databases/data lakes, with many users and tools constantly changing data, many users and tools out of their control, and an unknown/uncontrolled ETL/ELT process with no data quality tests. As a result, they are left with the blame for bad data and have limited ways to affect the actions of others who are changing the data. They need help to quickly identify anomalies and problems in the data before someone finds it.
This webinar discusses how to make embarrassing data errors a thing of the past.
We will start with how data engineers do not understand their data and have difficulty identifying problematic data records. We will also discuss how the vast majority of data engineers are so busy that they don’t know, or have time to write, tests to write to find data errors. We will finish with a demonstration of DataKitchen’s New DataOps Testgen Product.
That missing piece that connects data system expectations and reality is a ‘Data Journey.’ It is the missing piece of our data systems.
Demanding Data Consumers require a personalized level of Data Observability. As opposed to receiving one-size-fits-all status updates, these key stakeholders desire real-time, granular insights into the status of their specific data as it traverses the complicated data production pipeline. Learn why this is essential to your success.
Data analytic team war rooms, often convened for emergency problem-solving, epitomize inefficiency and detract from proactive, value-driven tasks. By leveraging data observability and rigorous testing, issues can be detected and resolved early, negating the need for such reactive measures in the modern era of DataOps.
The article illuminates how Data Journeys can enhance data governance, improve operational efficiency, and ultimately lead to organizational success by thoroughly examining different Data Journey types—’ Watcher,’ ‘Traveler,’ ‘Hub & Spoke,’ and ‘Payload.’
The uncertainty of not knowing where data issues will crop up next and the tiresome game of ‘who’s to blame’ when pinpointing the failure. This is where the true power of complete data observability comes into play, and it’s time to get acquainted with its two critical parts: ‘Data in Place’ and ‘Data in Use.’